Energy management is pursued at many levels today for different types of energy. In the electricity market there is local energy management at industrial sites or residences, and distribution grid and transmission grid energy management. In the natural gas market there is local management as well as management in the gas transmission system. But, grid operators find it increasingly challenging to manage aspects of their respective energy grids such as: balancing electricity supply with demand and responding to frequency shifts with respect to the electrical grid; and balancing supply with demand and responding to pressure changes with respect to the natural gas grid.
In general, a grid operator may mandate behavior of (or provide financial incentives for) its energy producers or its energy consumers in order to ensure a stable and responsive electrical grid or natural gas grid. Specifically, in the area of electricity, a grid operator may buy regulation capacity from industrial consumers and/or producers of power. A consumer or producer offering such a service will receive the mandate to reduce or increase their power consumption when required by the grid operator in order to maintain stability and quality of the grid. There may be a specific requirement that a reduction or increase in power consumption must be stable for a relatively long period of time, or that any such reduction or increase occurs rapidly. Importantly, a grid operator desires to manage loads at the portfolio level rather than at the individual load level.
A fast response time can be particularly important for an electricity grid operator. A grid operator must (by mandate) keep the frequency of the power offered on the grid stable (60 Hz in the United States and 50 Hz in Europe), but it can be challenging to keep the grid frequency within an allowable margin. For example, if a power plant is shut down unexpectedly, a large amount of power is suddenly unavailable (demand exceeds supply) and the frequency on the grid will decrease. Similarly, the frequency on the grid will drop if large industrial loads come on line and supply is slow to meet that demand. If the frequency of the grid decreases, the frequency can be brought to its reference level by reducing power consumption on the grid or by increasing the supply. But, it can be challenging to mandate a reduction in power consumption from among a diverse collection of industrial consumers. And, perhaps more importantly, it can be very difficult to achieve a reduction in power consumption as quickly as a grid operator seeks to achieve it—typically on the order of seconds, rather than on the order of minutes. A centralized management system may not be able to detect the deviation, schedule a reduction in power, and deliver the schedules to the industrial loads reliably in that short amount of time. The reverse can happen as well. When supply is larger than demand, as happens for instance in case of under-forecast of renewable power production, the frequency will rise above its reference level (50 Hz or 60 Hz). This can be offset either by decreasing the power production or by increasing the power consumption.
Similarly, in a natural gas grid, the pressure has to be kept at a certain reference level. Although it is much easier to store natural gas, there are cases where due to an unexpected event, grid pressure drops or rises. In that case, controlling the consumption at other (neighboring) sites is a solution to bringing the pressure back to its original reference level.
Prior art techniques include using a simple binary switch at a load that will switch off the entire load when the switch detects that the frequency of the power has dropped to a certain level (e.g., the load is switched off when the frequency drops to 49.9 Hz). But, this is a static technique in which the switch is an isolated hardware device that is locked into always switching off the load at a particular frequency; such a device might rigidly switch off the load in such a fashion for many months or years without taking other information into account. This technique also works “unilaterally,” in the sense that it does not allow the local operational managers to refuse “requests” for power activation based on operational or business constraints. Moreover, this technique is performed at the load level and does not benefit from any portfolio optimization.
U.S. Pat. No. 8,417,391, issued to Rombouts J. W. et al., “Automated demand-response energy management system,” is mostly concerned with determining the optimal control parameters for loads, and using these parameters to determine schedules. It does not discuss a frequency response, nor address how to overcome the inherent relative slow response of central control.
EP 2560136 A1, naming Massey J. S. et al., “Method, system and computer program product for scheduling demand events,” is mostly concerned with the scheduling of demand response events. This approach works “unilaterally,” in the sense that it does not allow the local operational managers to refuse “requests” for power activation based on operational or business constraints. Moreover, this technique is performed at the load level and does not benefit from any portfolio optimization.
EP 2595014 A2, naming Greene et al., “Staggering and feathering of demand response and energy supply change in a energy management of an electrical system,” performs peak shaving in a closed power system, but its loads are not triggered by a local state.
WO 2013010266, naming Metcalfe et al., “Method and system for providing local primary frequency response,” optimizes the local droop response by setting operational set points, but there is no central portfolio optimization.
Accordingly, there is a need for techniques to allow local loads to respond rapidly to changes in a grid characteristic (such as a frequency deviation on the electrical grid) while still allowing a grid operator to manage loads at the portfolio level.